Dr.P.S.Jagadeesh Kumar University of Cambridge, United Kingdom

1. Indian Journal of Science and Technology (IJST)
Volume 5, Issue 9, September 2012
ISSN: 0974 -5645 All Rights Reserved © 2012 IJST
3116
2
Block Classification Scheme of Compound
Images: A Hybrid Extension
P.S.Jagadeesh Kumar
University of Cambridge
United Kingdom
scanned or otherwise acquired images [2]. The main
Abstract— The proposed paper aims in developing an efficient
block classification using histogram for compressing compound
images that contain graphics, text and picture images. In this
paper, the given compound image is segmented into 8x8 blocks
and these blocks are used for the image classification. The
segmented blocks are classified into blocks of four different
types: background blocks, text blocks, mixed blocks and picture
blocks. The efficiency of block classification is observed to be of
97% for compound and computer generated images. The
proposed block classification method is very simple and
effective in compressing compound images. This paper discuss
about the block classification of compound images using
MATLAB.
Index Terms— Compound image, Image Segmentation, Block
Classification, Image compression
I. INTRODUCTION
A picture can say more than a thousand words.
Unfortunately, storing an image can cost more than a million
words. This isn't always a problem, because many of today's
computers are sophisticated enough to handle large amounts
of data. Sometimes however you want to use the limited
resources more efficiently. Digital cameras for instance often
have a totally unsatisfactory amount of memory, and the
internet can be very slow. Mostly in internet, it is necessary to
send the digital type of images using digital camera, personal
computers. It contains more and more compound images.
While sending the compound images, it occupies more size
and takes large amount of time to attach. In such conditions,
compound image compression is needed and thus requires
rethinking of our approach to compression.
In this paper, the block based segmentation approach is
considered and it gives the better result. In the case of object
based approach, complexity is the main drawback, since
image segmentation may require the use of very sophisticated
segmentation algorithms. In layer based segmentation, the
main drawbacks are mismatch between the compression
method and the data types, and an intrinsic redundancy due to
the fact that the same parts of the original image appear in
several layers. But in the block based segmentation it gives
the better mismatch between the region boundaries and the
compression algorithms, and the lack of redundancy. The
proposed block classification algorithm has low calculation
complexity, which makes it very suitable for real-time
application. From a practical point of view it is important to
differentiate between the computer-generated images and a
Manuscript received March, 2012.
Department of Computer Science
University of Cambridge, United Kingdom
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difference is that the acquired images will have a higher level
of inherent noise. This will impact both the segmentation
strategy, and the selection of the compression method.
Blocks of different type are distinct in nature and have
different statistics properties. Background blocks are very
flat and dominated by one kind of color. Text blocks are more
compact in spatial domain than that in DCT domain. The
picture block is mainly concentrated on low frequency
coefficients when they are DCT transformed. Mixed blocks,
containing mixed text and picture images, cannot be
compactly represented both in spatial and frequency domain.
II. BLOCK BASED COMPRESSION
Compressing the compound image is the hard problem
because it contains the combination of text, picture,
background and mixed types of blocks. It is well addressed in
JPEG2000 standard. In the past, compression research has
been on developing better algorithms, the future focus is
likely to be on the methods of combining various algorithms
to achieve the best compression performance for the given
types of images. A lot of algorithms have been designed to
compress compound images with different types. Run length
coding is well suitable for compressing the background
blocks. The Lempel-Ziv algorithm is designed to compress
pure text images, which only have text on the pure color
background in the whole images. As the text blocks are
images itself rather than text, implementing Lempel-Ziv
algorithm is not feasible and more complex. Wavelet
compression is suitable to compress the text blocks. The
JPEG algorithms are suitable for pure picture images. One
popular video coding standard H.264 [1] gives better
performance for the mixed blocks.
Fig.1 Block Based Compression
The framework of the block-based compression scheme is
shown in Fig.1. The compound image is first divided into 8x8
blocks. Then blocks are classified into four types:

2. Indian Journal of Science and Technology (IJST)
Volume 5, Issue 9, September 2012
ISSN: 0974 -5645 All Rights Reserved © 2012 IJST 3117
background, text, mixed and picture according to their
different statistical characteristics. Blocks of different type
will be compressed with different algorithms. The proposed
scheme can effectively compress the mixed blocks, which are
not well handled by some block-based algorithms. The
proposed scheme achieves good coding performance on text
images, picture images and compound images. It also
outperforms DjVu on compound images at high bit rate [3].
The block type map is compressed using an arithmeticcoder.
III. BLOCK SEGMENTATION
Segmentation subdivides an image into its constituent
regions or objects. Image segmentation is typically used to
locate objects and boundaries (lines, curves, etc.) in images.
More precisely, image segmentation is the process of
assigning a label to every pixel in an image such that pixels
with the same label share certain visual characteristics. The
result of image segmentation is a set of segments that
collectively cover the entire image, set of contours extracted
from the image. Each of the pixels in a region is similar with
respect to some characteristic or computed property, such
as color, intensity or texture [4]. Adjacent regions are
significantly different with respect to the same
characteristics. General approach to compress the compound
image includes the image segmentation into the regions of
similar data types. Bandwidth is a very important limiting
factor in application of image segmentation [5]. In this paper
the given image is segmented into 8x8 blocks and then that
blocks are used for the image classification process.
IV. BLOCK CLASSIFICATION SCHEME
Block classification is defined as to classify the blocks into
individual blocks. Classification is performed by using the
histogram values. A histogram is a graphical representation,
showing a visual impression of the distribution of
experimental data. Based upon the histogram, it is necessary
to set the threshold values. The threshold values classify the
blocks as text, picture, background and mixed blocks. A fast
and effective classification algorithm based on three features:
histogram, gradient of the block and the anisotropic values.
The entire block classification flow is shown in Fig.2. Here
the blocks are classified into four types: background, text,
mixed and picture. Blocks of different type are distinct in
nature and have different statistics properties. The
background blocks contain only the low histogram pixels and
show one peak at the low histogram pixels. Text blocks
always shows several peaks in low histogram value (LHV)
and the high histogram values (HHV) as shown in Fig.3.
Only a few mid-histogram values (MHV) are observed in text
blocks. If the block contains large numbers of high histogram
and mid histogram values, it will be identified as mixed
blocks. The block mainly consisting of mid histogram values
are declared as picture blocks. Here thresholds T1-T4 is
chosen to determine the block type.
V. BLOCK CODING
Block Coding consists of four algorithms to compress the
individual blocks. They are Wavelet, Run length encoding,
JPEG2000 algorithm, H.264 algorithm to compress the text,
background, picture and mixed blocks. Then by using
arithmetic coder all the individual blocks are get added [6].
Thus the compressed images are obtained. Then by using
decompression algorithm the original images is obtained
without any affects. Blocks of different type are distinct in
nature and have different statistics properties. Background
blocks are very flat and dominated by one kind of color
(white color). Text blocks are more compact in spatial
domain than that in DCT domain. The picture block is mainly
concentrated on low frequency coefficients when they are
DCT transformed. Mixed blocks containing mixed text and
picture images cannot be compactly represented both in
spatial and frequency domain.
A. Background Block Coding Algorithm
The coding of background blocks is straightforward.
Background blocks are dominated in the white region atonly
one point and gray scale levels are limited to the given
threshold values. All the values in the background block
regions are quantized to the most frequent color, which is
coded using run length encoder [7]. Run-length encoding
(RLE) is a very simple form of data compression in which
runs of data i.e. sequences in which the same data value
occurs in many consecutive data elements are stored as a
single data value and count, rather than as the original run.
This is most useful on data that contains many such runs: for
example, simple graphic images such as icons, line drawings,
animations and white spaces. It is not useful with files that
don't have many runs as it could greatly increase the file size.
Run-length encodingperforms lossless data compression and
is well suited to palette-based iconic images. It does not work
well at all on continuous-tone images such as photographs,
although JPEG uses it quite effectively on the coefficients
that remain after transforming and quantizing image blocks.
B. Text Block coding Algorithm
Wavelet coding is used to compress the text blocks.
Wavelet theory intends to analyze and transform data. It can
be used to make explicit the correlation between neighboring
pixels of an image, and this explicit correlation can be
exploited by compression algorithms to store the same image
more efficiently [8]. Wavelets can even be used to transform
an image in more and less important data items. By only
storing the important ones the image can be stored in an
amazingly more compact fashion, at the cost of introducing
hardly noticeable distortions in the image. As the text blocks
are images itself rather than text, implementing Lempel-Ziv
algorithm is not feasible and more complex [9]. Wavelet
based compression overcomes this problem and provides
efficient compression of text blocks.
C. Mixed Block coding Algorithm
The latest video compression standard, H.264 (also known
as MPEG-4 Part 10/AVC for Advanced Video Coding), is
expected to become the video standard of choice in the
coming years. H.264 is an open, licensed standard that
supports the most efficient video compression techniques
available today. Without compromising image quality, an
H.264 encoder can reduce the size of a digital video file by
more than 80% compared with the Motion JPEG format and
as much as 50% more than with the MPEG-4 Part 2 standard.
Context-adaptive binary arithmetic coding (CABAC) is a
form of entropy coding used in H.264/MPEG-4 AVC video
encoding. It is a lossless compression technique. It is notable
for providing much better compression than most other

3. Indian Journal of Science and Technology (IJST)
Volume 5, Issue 9, September 2012
ISSN: 0974 -5645 All Rights Reserved © 2012 IJST
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encoding algorithms used in video encoding, and is one of the
primary advantages of the H.264/AVC encoding scheme
[10]. Implementation of H.264 CABAC coding makes
efficient compression of mixed blocks.
Fig.2 Block Classification Scheme
D. Picture Block coding Algorithm
The aim of JPEG 2000 is not only improving compression
performance over JPEG but also adding (or improving)
features such as scalability and editability [9]. In fact, JPEG
2000's improvement in compression performance relative to
the original JPEG standard is actually rather modest and
should not ordinarily be the primary consideration for
evaluating the design. Very low and very high compression
rates are supported in JPEG 2000. In fact, the gracefulability
of the design to handle a very large range of effective bit rates
is one of the strengths of JPEG 2000. For example, to reduce
the number of bits for a picture below a certain amount, the
advisable thing to do with the first JPEG standard is to reduce
the resolution of the input image before encoding it [11].
That's unnecessary when using JPEG 2000, because JPEG
2000 already does this automatically through its multi-
resolution decomposition structure. Compared to the
previous JPEG standard, JPEG 2000 delivers a typical
compression gain in the range of 20%, depending on the
image characteristics. Higher-resolution images tend to
benefit more, where JPEG-2000's spatial-redundancy
prediction can contribute more to the compression process
[12-14]. In very low-bitrate applications, studies have shown
JPEG 2000 to be outperformed by the intra-frame coding
mode of H.264. Implementation of JPEG2000 makes
compression of picture blocks more easy and effective.
Fig.3 Three histograms of Picture block (top),
Text block (middle) and Mixed block (bottom)
VI. EXPERIMENTAL RESULT
The famous toy store compound image as shown in Fig.4 is
taken as the input image to our proposed block classification
scheme. The segmented image is shown in the Fig. 5. The
block classification for text, mixed, background and picture
blocks are shown in the Fig. 6,7,8,9 respectively. The
proposed system has been simulated in MATLAB
SIMULINK environment.

4. Indian Journal of Science and Technology (IJST)
Volume 5, Issue 9, September 2012
ISSN: 0974 -5645 All Rights Reserved © 2012 IJST 3119
VII. CONCLUSION
The block classification scheme was tested for many such
compound and computer generated images. It was observed
from table1 that the proposed block classification scheme is
97% efficient for compound images. However it failed to
make the same consistency for other type of images.
Practically there is no need to classify other type of images as
they can be effectively compressed as a whole. Considering
the fact that sensitivity for human eyes can negotiate this 3%
mismatch of block classification, our block classification
scheme can be argued to be an efficient block classification
scheme for compressing compound images. Our block
classification scheme is very simple and effective, reducing
the computational complexity.
Fig.4 Input image
Fig.5 Segmented Image
Fig.6 Classified text blocks
Fig.7 Classified mixed blocks
Fig.8 Classified background blocks
Fig.9 Classified Picture Blocks
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5. Indian Journal of Science and Technology (IJST)
Volume 5, Issue 9, September 2012
ISSN: 0974 -5645 All Rights Reserved © 2012 IJST
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[5] A. Said and A. Drukarev, “Simplified segmentation for compound
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S.No Compound
Image
Background Block Text Block Picture Block Mixed Block Efficiency
Actual Identified Actual Identified Actual Identified Actual Identified
1. Comp1 12 12 7 7 5 4 40 41 96.9%
2. Comp2 20 20 13 12 8 10 23 24 93.8%
3. Comp3 14 14 16 16 17 16 17 18 96.9%
4. Comp4 15 15 9 9 10 9 30 31 96.9%
5. Slide1 10 10 17 17 15 15 22 22 100%
6. Slide2 11 11 18 18 12 14 23 21 93.8%
7. Poster1 15 15 10 9 11 11 28 29 96.9%
8. Poster2 18 18 8 8 8 9 30 29 96.9%
9. Desktop1 29 29 10 10 10 9 15 16 96.9%
10. Desktop2 23 23 11 11 9 9 21 21 100%
Overall Efficiency 96.9%
Table.1 Comparison between the actual and identified background, text, picture and mixed blocks of
proposed block classification scheme for different compound images